Distributed Robust Cubature Information Filtering for Measurement Outliers in Wireless Sensor Networks
In wireless sensor networks (WSN), measurements are always corrupted by outliers or impulsive noise. Cubature information filtering (CIF) is founded based on minimum mean square error (MMSE) criterion, which is not applicable to non-Gaussian noise. Hence, a novel robust CIF (RCIF) is derived based o...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8966292/ |
| _version_ | 1818330288645734400 |
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| author | Jiahao Zhang Shesheng Gao Xiaomin Qi Jiahui Yang Juan Xia Bingbing Gao |
| author_facet | Jiahao Zhang Shesheng Gao Xiaomin Qi Jiahui Yang Juan Xia Bingbing Gao |
| author_sort | Jiahao Zhang |
| collection | DOAJ |
| description | In wireless sensor networks (WSN), measurements are always corrupted by outliers or impulsive noise. Cubature information filtering (CIF) is founded based on minimum mean square error (MMSE) criterion, which is not applicable to non-Gaussian noise. Hence, a novel robust CIF (RCIF) is derived based on maximum correntropy criterion (MCC) to enhance the robustness of state estimation in the local node. For the information fusion, weighted average consensus (WAC) based distributed RCIF (DRCIF) is founded to improve the stability of sensor networks and the accuracy of state estimation. The estimation error of DRCIF is proved to be bounded in mean square. Numerical simulations are provided to evaluate the effectiveness of proposed algorithms. |
| first_indexed | 2024-12-13T13:01:35Z |
| format | Article |
| id | doaj.art-04c79ee51e264b8688f04556f51a554c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T13:01:35Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-04c79ee51e264b8688f04556f51a554c2022-12-21T23:44:58ZengIEEEIEEE Access2169-35362020-01-018202032021410.1109/ACCESS.2020.29686028966292Distributed Robust Cubature Information Filtering for Measurement Outliers in Wireless Sensor NetworksJiahao Zhang0https://orcid.org/0000-0001-9421-2415Shesheng Gao1https://orcid.org/0000-0002-7980-9085Xiaomin Qi2https://orcid.org/0000-0001-6343-6707Jiahui Yang3https://orcid.org/0000-0002-2048-1855Juan Xia4https://orcid.org/0000-0001-8391-5262Bingbing Gao5https://orcid.org/0000-0002-6562-9315Research & Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen, ChinaResearch & Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen, ChinaDepartment of Electrical and Computer Engineering, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, PakistanResearch & Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen, ChinaResearch & Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen, ChinaResearch & Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen, ChinaIn wireless sensor networks (WSN), measurements are always corrupted by outliers or impulsive noise. Cubature information filtering (CIF) is founded based on minimum mean square error (MMSE) criterion, which is not applicable to non-Gaussian noise. Hence, a novel robust CIF (RCIF) is derived based on maximum correntropy criterion (MCC) to enhance the robustness of state estimation in the local node. For the information fusion, weighted average consensus (WAC) based distributed RCIF (DRCIF) is founded to improve the stability of sensor networks and the accuracy of state estimation. The estimation error of DRCIF is proved to be bounded in mean square. Numerical simulations are provided to evaluate the effectiveness of proposed algorithms.https://ieeexplore.ieee.org/document/8966292/Robust cubature information filteringmaximum correntropy criterionNon-Gaussian measurement noisedistributed state estimationweighted average consensus |
| spellingShingle | Jiahao Zhang Shesheng Gao Xiaomin Qi Jiahui Yang Juan Xia Bingbing Gao Distributed Robust Cubature Information Filtering for Measurement Outliers in Wireless Sensor Networks IEEE Access Robust cubature information filtering maximum correntropy criterion Non-Gaussian measurement noise distributed state estimation weighted average consensus |
| title | Distributed Robust Cubature Information Filtering for Measurement Outliers in Wireless Sensor Networks |
| title_full | Distributed Robust Cubature Information Filtering for Measurement Outliers in Wireless Sensor Networks |
| title_fullStr | Distributed Robust Cubature Information Filtering for Measurement Outliers in Wireless Sensor Networks |
| title_full_unstemmed | Distributed Robust Cubature Information Filtering for Measurement Outliers in Wireless Sensor Networks |
| title_short | Distributed Robust Cubature Information Filtering for Measurement Outliers in Wireless Sensor Networks |
| title_sort | distributed robust cubature information filtering for measurement outliers in wireless sensor networks |
| topic | Robust cubature information filtering maximum correntropy criterion Non-Gaussian measurement noise distributed state estimation weighted average consensus |
| url | https://ieeexplore.ieee.org/document/8966292/ |
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